Machine Learning in Employment Research Considering Methods and Applications in Job Quality and Algorithmic Management

Nikita Kalganov, B. Sabdenaliyev, A. Igenbayev, Murat G. Kozhanov, Amir Hosein Mosavi, Csaba Makó, Kazizat ISKAKOV, A. Shakhatova

2025

Closed access Metadata (CSL-JSON)

DOI 10.1109/cinti67731.2025.11311753

Data confidence 70 medium

How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This review looks at the implementation of artificial intelligence (AI) and machine learning (ML) into Employment research. Based upon an extensive search of literature, the study aims to illustrate the main themes of algorithmic management, platform labor, and occupational safety. An initial search concerning publications between 2014 and 2024 followed an in-depth screening procedure. This review proposes a taxonomy of AI in employment research and demonstrates its role in modeling the employment quality and organizational productivity as well as the occupational safety. The results highlight the essential aspects of AI on the nature of work. The research contributes to the understanding of the impact of ML on employees and organizations.